The present invention relates to a system and methods for a business tool for determining likelihood of customer attrition, and developing retention stratagems to prevent the customer loss. This business tool may be stand alone, or may be integrated into a pricing optimization system to provide more effective pricing of products. More particularly, the present customer attrition identifier and retainer system may predict a particular customer's likelihood of loss and develop measures which retain the customer before any significant financial loss is experienced from the customer's changing behaviors.
For a business to properly and profitably function there must be relatively constant customer base to ensure a steady revenue stream. For businesses that cater to short-lifespan consumer goods, profitability is only assured when there is repeated customer patronage. Examples of such businesses that rely upon repeated consumer visits include supermarket and food sellers, department stores, movie theaters, most restaurants, and all other “small item” sellers.
To ensure a constant consumer base, businesses may engage in campaigns intended to draw more customers. Additionally, businesses rely upon customer retention and loyalty to maintain existing customers.
It has been traditionally the case that retaining an existing customer is significantly less costly than getting a new customer. In many businesses, this expense discrepancy between getting a new customer and keeping an old customer may be as high as an order of magnitude. Thus, many companies and businesses have attempted to generate the best customer service economically possible, reduce prices and develop a pleasurable shopping experience in order to keep existing customers.
However, such global means of customer retention may not be adequate to retain all customers. All businesses have some level of customer attrition. In the supermarket industry, there may be a loss of roughly 3% to 20% of customers. This loss rate may vary on business locale, type, quality and business model. This customer loss may cost a business dearly over the long term in lost patronage, referrals, and costs associated with generating new customers.
Traditionally, to prevent this customer loss, or attrition, a business simply “tried its best” to ensure good customer relations, and when a disgruntled customer was identified, a manager or similar employee would spend individual attention with the upset customer to appease him, and hopefully maintain that customer's loyalty.
The problem with traditional customer retention practices is that it suffers from diminishing returns. Enhancing customer service greatly above industry standards may result in a reduction in customer loss, for example, from 6% to 4%; however, the costs of a global improvement in customer service may greatly outweigh these gains. Thus, most businesses maintain a level of customer service which optimally balances costs and retention. Further reduction in retention is uneconomical due to diminishing returns of global customer retention measures.
However, the cost of customer retention may be greatly reduced if the business is able to target the customer who is likely to leave. Thus, the upset customer may receive a discount, or “freebie”, and enhanced employee attention in order to keep the customer. This level of customer service is uneconomical on a global scale; however, when an upset or disgruntled customer is identified, such more costly retention measures may be economically applied.
Unfortunately for businesses, determining the customers who are likely to leave traditionally required the customer to become upset enough to communicate their displeasure with the business. Alternatively, identification by the business of customers likely to leave has typically relied upon frequency measures of a customer's patronage. While these frequency measures are very accurate, leaving customers are only identified after loss has occurred. At this point, retaining the customer may not be possible.
Were the likelihood of attrition of the customer determinable before loss occurs, more effective retention measures would be able to be imposed.
It is therefore apparent that an urgent need exists for improved customer retention methods. This improved customer retention requires accurately predicting a customer's likelihood of attrition before significant customer loss has occurred. This customer retention system would be able to provide businesses with an advanced competitive tool to greatly reduce customer loss in a cost efficient manner.